After IMD, Skymet predicts good monsoon in central India

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A region-wise forecast released by Skymet Weather, a private weather forecaster, predicts that central India will see a particularly good monsoon this year.

This follows the India Meteorological Department (IMD)’s announcement forecasting a normal southwest monsoon in India. The IMD considers it a normal monsoon when rainfall is between 96-104% of the 50-year average of 887mm, also called the Long Period Average (LPA).

Skymet Weather said earlier this month that rainfall will be 100% of the Long Period Average (LPA) with 0% chance of drought.

Central India will get rainfall that is 108% of its LPA, as per their latest dispatch.

The April IMD forecast only provides average rainfall predictions for the whole country.

The IMD shares predictions for four broad divisions — northwest, east and northeast, peninsular south and central India in June.

However, the error margins for these forecasts is very high (+/-9%). “Regional forecasting is a risky business,” Mahesh Palawat, Skymet Weather’s chief meteorologist said.

The Skymet forecast also comes with a wide error margin of +/- 8%.

National averages mask regional distributions that impact state and local economies and fates of farmers.

Parts of north Maharashtra and Madhya Pradesh, Konkan and Chhattisgarh will see a strong monsoon, while the east and northeast will get below normal rainfall.

“We have been generating state-wise experimental forecasts for two years that are communicated to the states who ask for them,” Madhavan Rajeevan, secretary, ministry of earth sciences, said. “In another year, we might be able to operationalise state-wise forecasts.”

However, senior officials at the IMD were skeptical about generating state-wise forecasts with a reasonable level of accuracy any time soon.

“We have not acquired the level of accuracy to operationalise the forecasts,” KJ Ramesh, director general of IMD, said. “Our error margin for the homogenous region wise forecast is +/- 9%. We are trying to improve the accuracy before looking at smaller divisions.”